Measuring aversion to debt: an experiment among student loan candidates
Bibliographic record
Abstract
This paper reports the results of an experiment designed to test for the presence of debt aversion. The population who participated in the experiment were recent financial aid candidates and the experiment focused on student loans. The goal is to shed new light on different aspects of the perceptions with respect to debt. These perceptions can prevent agents from choosing an optimal portfolio or from undertaking attractive investment opportunities, such as in education. The study design disentangles two types of debt aversion: one that is studied in the previous literature, which encompasses both framing and labeling effects, and another that controls for framing effects and identifies only what we denote labeling debt aversion. The results suggest that participants in the experiment exhibit debt aversion, and most of the debt aversion is due to labeling effects. Labeling a contract as a"loan"'decreases its probability of being chosen over a financially equivalent contract by more than 8 percent. The analysis also provides evidence that students are willing to pay a premium of about 4 percent of the financed value to avoid a contract labeled as debt.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".